Multisensor Track Termination for Targets with Fluctuating SNR

In active sonar tracking applications, targets frequently undergo fading detection performance in which the target´s detection probability can shift suddenly between high and low values. Using a multistatic active sonar problem, we examine the performance of sequential track termination tests where target detections are based on an underlying hidden Markov model (HMM) with high and low detection states. We show that the Page test is not optimal in this problem and that a K/N track termination rule yields better performance. Further we show that a Bayesian sequential test (the Shiryaev test) yields dramatic performance improvements over both the K/N rule and the Page test.